Abstract
NK-landscapes are functions that allow the degree of epistasis to be tuned and are defined in terms of N, the length of the bit string and K, the number of bits that contribute to the evaluation of each of the N loci in the string. NK-landscapes have been the focus of numerous theoretical and empirical studies in the field of evolutionary computation. Despite all that has been learned from these studies, there are still many open questions concerning NK-landscapes. Most of these studies have been performed using very small problems and have neglected to benchmark the performances of genetic algorithms (GA) with those of hill-climbers. Heckendorn, Rana, and Whitley performed initial investigations addressing these questions for NK-landscapes where N=100, concluding that an enhanced random bit-climber was best for solving NK-landscapes. Replicating and extending their work, it is concluded that a niche exists for genetic algorithms (GAs) like CHC in the NK-landscape functions and describes the bounds of this niche.
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